class_docstr: Cost model to predict the speed of a config
docstr: Fit to training data
  
  Parameters
  ----------
  xs: Array of int
      indexes of configs in the config space
  ys: Array of float
      The speed (flop, float number operations per second)
  plan_size: int
      The plan size of tuner
docstr_tok: ["Fit","to","training","data","Parameters","--","--","--","--","--","xs",":","Array","of","int","indexes","of","configs","in","the","config","space","ys",":","Array","of","float","The","speed","(","flop",",","float","number","operations","per","second",")","plan_size",":","int","The","plan","size","of","tuner"]
code: def fit(self, xs, ys, plan_size):
      """Fit to training data
  
      Parameters
      ----------
      xs: Array of int
          indexes of configs in the config space
      ys: Array of float
          The speed (flop, float number operations per second)
      plan_size: int
          The plan size of tuner
      """
      raise NotImplementedError()
  
imports: import gc
  import numpy
  from .tuner import Tuner
  from ..env import GLOBAL_SCOPE
label_desc: Use image node_id 2 for calling the CostModel obj's underlying member method code with example usage: obj.fit(xs, ys, plan_size) without return types